For several years, there have been large research activities on variational image processing. In variational image processing, it is common to adopt a partial differential equation (PDE) in a given problem domain. With the appropriate PDE, the variational image processing is able to be realized using an iteration method to produce an improvement in various fields.
Although variational processing is known as a tool that is able to provide various image processing functionality, there are several disadvantages:
1. Iteration-Based Processing
The variational method is an iteration-based method. In other words, the solution of PDE is able to be solved only by iterations. As the iteration increases, more accurate results are able to be achieved. However, due to this iteration, a significant processing delay is unavoidable.
2. Frame-Based Processing
The variational method is a frame-based method. In other words, it requires that all of the frame data is available. However, in a hardware implementation, it is too expensive to provide such a big memory in System On-Chip (SOC). Practically, due to the fact that the frame memory is not able to be implemented in SOC, the intermediate results are stored in ‘external’ memory.
A major problem with frame-based processing in variational image processing is that the transaction image data is too large because one iteration requires one transaction of a whole image. FIG. 1 shows a diagram using an entire image for each iteration. In each transaction 100, an image 102 is transferred via a bus 106 and after the computation is stored as a stored image 104 which uses a significant amount of bandwidth.
If the required iteration is N, the required bandwidth will be:Required Transaction=N×Frame TransactionIn general, N is more than 100. Therefore, this transactional bandwidth is too high for practical implementation.